Neural Network Deinterlacing Using Multiple Fields and Field-MSEs

Abstract

Generally, deinterlacing algorithms can be either classified as intra methods or inter methods. Intra methods interpolate missing lines by using surrounding pixels in the current field. Inter methods interpolate missing lines by using pixels and the motion information of multiple fields. Neural network deinterlacing that uses multiple fields has been proposed. It provides improved performance compared to existing neural network deinterlacing algorithms that use a single field. However, when adjacent fields are very different, neural network deinterlacing that uses multiple fields may not provide good performance. To address this problem, we propose using field-MSE values as additional inputs. These MSE values can provide helpful information so that the networks can consider field differences in using multiple fields. Experimental results show that the use of the proposed algorithm results in improved performance.